Market Risk

We provide a descriptive examination of the trading activities of one of the most important intermediaries in global financial markets—the OTC derivatives dealer. These dealers play a central role in the provision of derivative products and in the intermediation of market risks faced by financial and non-financial firms alike. Utilizing a unique database, we analyze the derivatives holdings of 264 dealers spanning 34 countries over the period 1995-2001. We document the geographic composition of dealers on both country and regional levels as well as analyze trends in dealer holdings on an aggregate and individual product level. We further analyze the extent of global merger activity among dealers and resulting consolidation effects. Finally, we investigate at the individual dealer level the extent and evolution of their array of product offerings.

The key findings of the study are that monopoly power in the stock market can be created and exercised through information-based market manipulation by a strategic trader. Successful execution of manipulation can result in substantial profit to the strategic trader but unfair losses to other investors, as well as excess volatility to the manipulated stocks. In addition, we propose that creation and exercise of monopoly power in the stock market is one mechanism that contributes to a widening income gap between market manipulators and the investing public.

One of the main concerns of investors and financial managers is the way of dealing with investment risk; thus identification, calculation and management of risk are important issues in financial fields. So, in this study, the portfolio value-at-risk and expected shortfall are estimated by considering uncertainty in risk factors. The concept of fuzzy random variable, specifically possibility and necessity theory, is used to face uncertainty in financial data. In the following, the terms of fuzzy value-at-risk and expected shortfall introduced with assuming normal and t-student distribution and considering both state of fixed and stochastic for uncertainty factor. The results indicate that assumptions of t-student distribution and stochastic uncertainty factor make the estimation of both risk measures to be more conservative.

The efficient frontier is a core concept in Modern Portfolio Theory. Based on this idea, we will construct optimal trading curves for different types of portfolios. These curves correspond to the algorithmic trading strategies that minimize the expected transaction costs, i.e. the joint effect of market impact and market risk. We will study five portfolio trading strategies. For the first three (single-asset, general multi-asseet and balanced portfolios) we will assume that the underlyings follow a Gaussian diffusion, whereas for the last two portfolios we will suppose that there exists a combination of assets such that the corresponding portfolio follows a mean-reverting dynamics. The optimal trading curves can be computed by solving an N-dimensional optimization problem, where N is the (pre-determined) number of trading times. We will solve the recursive algorithm using the "shooting method", a numerical technique for differential equations. This method has the advantage...

This paper gives a summary of all the key elements that make up Enterprise Risk Management and its integration in key organizational business activities.
Enterprise risk management (ERM) is the process of planning, organizing, leading, and controlling the activities of an organization to minimize the effects of risk on an organization's capital and earnings, reputation and shareholder value. The benefit of ERM is that it aligns organization, people, processes and infrastructure, provides a benchmark for risk/reward, aids risk visibility to operational activities and for the more mature benefit, a competitive advantage. When ERM is sound business management if it becomes an integral part of the organization’s DNA.

El presente Trabajo Fin de Master plantea como objetivo la aplicación práctica del modelo GARCH y distribuciones de colas anchas en el cálculo del riesgo de mercado, mediante metodología VeR Paramétrica, en una cartera de renta variable en el periodo de 1/1/2014 al 31/12/2018 para los principales títulos del sector bancario y financiero que cotizan en el mercado bursátil español y que pertenecen al IBEX-35. Se escogen los valores cotizados de Santander, BBVA, CaixaBank, Sabadell y Bankia. Se realiza metodología empírica mediante técnicas matemáticas, estadísticas y econométricas. Se comparan resultados obtenidos entre distribución normal y distribución T-Student (colas anchas), y según la forma de cálculo de la volatilidad (Estándar o GARCH). Finalmente se realiza el Backtesting en el periodo de 1/1/2019 al 30/6/2019 para los resultados de la cartera y se establecen las conclusiones finales del estudio.

Aquaculture in China accounts for nearly 70% of world aquaculture production. Aquaculture, including a wide variety of freshwater and marine fishes, shellfish, crustaceans, and aquatic plants, has become one of the most vital primary industries and a center of economic activity within the local and global economies. Along with the development of aquaculture, concerns come about such problems in the

Financial time series analysis deals with the understanding of data collected on financial markets. Several parametric distribution models have been entertained for describing, estimating and predicting the dynamics of financial time series. Alternatively, this article considers a Bayesian semiparametric approach. In particular, the usual parametric distributional assumptions of the GARCH-type models are relaxed by entertaining the class of location-scale mixtures

Machine learning and artificial intelligence are big topics in the financial services sector these days. Financial institutions (FIs) are looking to more powerful analytical approaches in order to manage and mine increasing amounts of regulatory reporting data and unstructured data, for purposes of compliance and risk management (applying machine learning as " RegTech ") or in order to compete effectively with other FIs and FinTechs. This article aims to give an introduction to the machine learning field and discusses several application cases within financial institutions, based on discussions with IIF members and technology ventures: credit risk modeling, detection of credit card fraud and money laundering, and surveillance of conduct breaches at FIs. Two tentative conclusions emerge on the added value of applying machine learning in the financial services sector. First, FinTech/RegTech the ability of machine learning methods to analyze very large amounts of data, while offering a high granularity and depth of predictive analysis, can improve analytical capabilities across risk management and compliance areas in FIs. Examples are the detection of complex illicit transaction patterns on payment systems and more accurate credit risk modeling. Second , the application of machine learning approaches within the financial services sector is highly context-dependent. Ample , high-quality data for training or analysis are not always available in FIs. More importantly, the predictive power and granularity of analysis of several approaches can come at the cost of increased model complexity and a lack of explanatory insight. This is an issue particularly where analytics are applied in a regulatory context, and a supervisor or compliance team will want to audit and understand the applied model.

The main purpose of this report is to estimate the CAPM model using regression analysis. The capital asset pricing model (CAPM) is a model used to determine a theoretically appropriate required rate of return of an asset, to make decisions about adding assets to a well-diversified portfolio. For the purpose of CAPM calculation we have selected Bata Bangladesh. 5 year monthly data has been considered to calculate the beta of CAPM model which indicate the market risk of a company. Finally, we have estimated β is 0.58 which indicate that Bata Bangladesh is less sensitive relative to its market.

This paper investigates the effects of interest rate and foreign exchange rate changes on Turkish banks' stock returns using the OLS and GARCH estimation models. The results suggest that interest rate and exchange rate changes have a negative and significant impact on the conditional bank stock return. Also, bank stock return sensitivities are found to be stronger for market return than interest rates and exchange rates, implying that market return plays an important role in determining the dynamics of conditional return of bank stocks. The results further indicate that interest rate and exchange rate volatility are the major determinants of the conditional bank stock return volatility.► We investigate the effects of interest and exchange rates changes on banks' stock returns. ► We found that interest and exchange rates changes have negative effect on stock return. ► The major determinants of stock return volatility are interest and exchange rates volatility.

This is an attempt to empirically investigate the risk and return relationship of individual stocks traded at Karachi Stock Exchange (KSE), the main equity market in Pakistan. The analysis is based on daily as well as monthly data of 49 companies and KSE 100 index is used as market factor covering the period from July 1993 to December 2004. The

The PRIX index value for Q4 2016 is 44.09, based on input from 268 country analysts. This is the lowest PRIX index value ever registered, indicating a significant reduction in the flow of oil onto international markets and promoting an oil price rebound. The drop of 3.57 index points from Q3 to Q4 is the largest quarterly drop ever registered by the index, strengthening the prospects for a fall in global oil exports. It is the third quarter in a row that the index is significantly below a neutral value of 50, indicating a clear trend over time towards a weakening global supply side in oil markets. However, the impact on the oil price also depends on other factors, and in particular how much of the gap is filled by a rebound of US shale oil production. Kuwait, Saudi Arabia and Venezuela are among the biggest exporters that have seen major drop s in their index values, pulling the global index downwards. At 93.75, Libya has the highest index value of all of the countries, pointing towards a possible end to its export problems. Iran has bounced back to a high index value of 85.00, meaning that the Iran Country Analysts continue to expect rapidly rising exports from Iran.